"""simple docstring"""



import argparse
import json
import logging
import os
import sys
from unittest.mock import patch

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


SCREAMING_SNAKE_CASE__:List[Any]									=			[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        """text-classification""",
        """language-modeling""",
        """summarization""",
        """token-classification""",
        """question-answering""",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
   import run_clm_flax
   import run_flax_glue
   import run_flax_ner
   import run_mlm_flax
   import run_qa
   import run_summarization_flax
   import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

SCREAMING_SNAKE_CASE__:List[Any]									=			logging.getLogger()



def 	_lowerCamelCase(	):
       __a       =    argparse.ArgumentParser()
       parser.add_argument("-f"				)
       __a       =    parser.parse_args()
       return args.f



def 	_lowerCamelCase(	a   ,    a="eval"				):
       __a       =    os.path.join(a   ,    F"{split}_results.json"				)
       if os.path.exists(a				):
              with open(a   ,    "r"				) as f:
                     return json.load(a				)
       raise ValueError(F"can't find {path}"				)


SCREAMING_SNAKE_CASE__:Union[str, Any]									=			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class  snake_case__      (       snake_case_       ):


 def 						a__   ( self						):
        __a       =    self.get_auto_remove_tmp_dir()
        __a       =    F"\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

        with patch.object(lowerCamelCase    ,		"argv"    ,		lowerCamelCase						):
               run_flax_glue.main()
               __a       =    get_results(lowerCamelCase						)
               self.assertGreaterEqual(result["eval_accuracy"]    ,		0.75						)


 @slow
 def 						a__   ( self						):
        __a       =    self.get_auto_remove_tmp_dir()
        __a       =    F"\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

        with patch.object(lowerCamelCase    ,		"argv"    ,		lowerCamelCase						):
               run_clm_flax.main()
               __a       =    get_results(lowerCamelCase						)
               self.assertLess(result["eval_perplexity"]    ,		100						)


 @slow
 def 						a__   ( self						):
        __a       =    self.get_auto_remove_tmp_dir()
        __a       =    F"\n            run_summarization.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --test_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=8\n            --do_train\n            --do_eval\n            --do_predict\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --predict_with_generate\n        ".split()

        with patch.object(lowerCamelCase    ,		"argv"    ,		lowerCamelCase						):
               run_summarization_flax.main()
               __a       =    get_results(lowerCamelCase    ,		split="test"						)
               self.assertGreaterEqual(result["test_rouge1"]    ,		10						)
               self.assertGreaterEqual(result["test_rouge2"]    ,		2						)
               self.assertGreaterEqual(result["test_rougeL"]    ,		7						)
               self.assertGreaterEqual(result["test_rougeLsum"]    ,		7						)


 @slow
 def 						a__   ( self						):
        __a       =    self.get_auto_remove_tmp_dir()
        __a       =    F"\n            run_mlm.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --logging_steps 2 --eval_steps 2\n            --do_train\n            --do_eval\n            --num_train_epochs=1\n        ".split()

        with patch.object(lowerCamelCase    ,		"argv"    ,		lowerCamelCase						):
               run_mlm_flax.main()
               __a       =    get_results(lowerCamelCase						)
               self.assertLess(result["eval_perplexity"]    ,		42						)


 @slow
 def 						a__   ( self						):
        __a       =    self.get_auto_remove_tmp_dir()
        __a       =    F"\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

        with patch.object(lowerCamelCase    ,		"argv"    ,		lowerCamelCase						):
               run_ta_mlm_flax.main()
               __a       =    get_results(lowerCamelCase						)
               self.assertGreaterEqual(result["eval_accuracy"]    ,		0.42						)


 @slow
 def 						a__   ( self						):
        # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
        __a       =    7 if get_gpu_count() > 1 else 2

        __a       =    self.get_auto_remove_tmp_dir()
        __a       =    F"\n            run_flax_ner.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --do_train\n            --do_eval\n            --warmup_steps=2\n            --learning_rate=2e-4\n            --logging_steps 2 --eval_steps 2\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n        ".split()

        with patch.object(lowerCamelCase    ,		"argv"    ,		lowerCamelCase						):
               run_flax_ner.main()
               __a       =    get_results(lowerCamelCase						)
               self.assertGreaterEqual(result["eval_accuracy"]    ,		0.75						)
               self.assertGreaterEqual(result["eval_f1"]    ,		0.3						)






 @slow
 def 						a__   ( self						):
        __a       =    self.get_auto_remove_tmp_dir()
        __a       =    F"\n            run_qa.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            --num_train_epochs=3\n            --warmup_steps=2\n            --do_train\n            --do_eval\n            --logging_steps 2 --eval_steps 2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n        ".split()

        with patch.object(lowerCamelCase    ,		"argv"    ,		lowerCamelCase						):
               run_qa.main()
               __a       =    get_results(lowerCamelCase						)
               self.assertGreaterEqual(result["eval_f1"]    ,		30						)
               self.assertGreaterEqual(result["eval_exact"]    ,		30						)

"""simple docstring"""



import warnings

from ...utils import logging
from .image_processing_deformable_detr import DeformableDetrImageProcessor


SCREAMING_SNAKE_CASE__:int									=			logging.get_logger(__name__)






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		*lowerCamelCase    ,		**lowerCamelCase						):
        warnings.warn(
            "The class DeformableDetrFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
            " Please use DeformableDetrImageProcessor instead."    ,		lowerCamelCase    ,		)
        super().__init__(*lowerCamelCase    ,		**lowerCamelCase						)
